Better segment aerial photo by learning multi-resolution features with IFWM
Better segment aerial photo by learning multi-resolution features with IFWM
Improved-Flow Warp Module for Remote Sensing Semantic Segmentation
arXiv paper abstract https://arxiv.org/abs/2205.04160
arXiv PDF paper https://arxiv.org/ftp/arxiv/papers/2205/2205.04160.pdf
Remote sensing semantic segmentation aims to assign automatically each pixel on aerial images with specific label.
… proposed a new module, called improved-flow warp module (IFWM), to adjust semantic feature maps across different scales
… The improved-flow warp module is applied along with the feature extraction process in the convolutional neural network.
First, IFWM computes the offsets of pixels by a learnable way, which can alleviate the misalignment of the multi-scale features.
Second, the offsets help with the low-resolution deep feature up-sampling process to improve the feature accordance, which boosts the accuracy of semantic segmentation.
… validate … method on several remote sensing datasets, and the results prove the effectiveness of … method.
Stay up to date. Subscribe to my posts https://morrislee1234.wixsite.com/website/contact
Web site with my other posts by category https://morrislee1234.wixsite.com/website
LinkedIn https://www.linkedin.com/in/morris-lee-47877b7b
